# SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography

arXiv: https://arxiv.org/abs/2404.06869 · HTML: https://mlsanity.com/paper/2404.06869/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Uncertainty Modeling for Out-of-Distribution Generalization](https://mlsanity.com/paper/2202.03958/) (2022)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks](https://mlsanity.com/paper/2303.11338/) (2023)
- [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography](https://mlsanity.com/paper/2202.05735/) (2022)
- [Generalization in medical AI: a perspective on developing scalable models](https://mlsanity.com/paper/2311.05418/) (2023)

## Abstract (arXiv)

Background: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Traditionally, this analysis is conducted in clinical settings and involves a time-consuming scoring procedure. Recent data-driven algorithms for sleep staging, using the photoplethysmogram (PPG) time series, have shown high performance on local test sets but lower performance on external datasets due to data drift. Methods: This study aimed to develop a generalizable deep learning model for the task of four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from raw PPG physiological time-series. Six sleep datasets, totaling 2,574 patients recordings, were used. In order to create a more generalizable representation, we developed and evaluated a deep learning model called SleepPPG-Net2, which employs a multi-source domain training approach.SleepPPG-Net2 was benchmarked against two state-of-the-art models. Results: SleepPPG-Net2 showed consistently higher performance over benchmark approaches, with generalization performance (Cohen's kappa) improving by up to 19%. Performance disparities were observed in relation to age, sex, and sleep apnea severity. Conclusion: SleepPPG-Net2 sets a new standard for staging sleep from raw PPG time-series.